Mobile Visual Recognition via Offline Feature Extraction
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Solution Overview
Problem
Mobile devices face challenges in performing real-time location recognition due to limited storage capacity, computational resources, and bandwidth constraints, leading to latency issues when relying on remote servers for image processing.
Innovation Solution
A mobile device performs real-time location recognition with minimal server assistance by using a compact classifier and feature descriptors generated through offline training, specifically a random decision forest, which reduces computational processing and minimizes data transmission by selecting discriminative features for efficient image classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the mobile device uploads images to a server for location recognition, then the recognition accuracy is improved, but the bandwidth consumption and latency increase
Solution Approach 1:
The server performs preliminary actions by generating compact classifiers and feature descriptors based on geophysical location data before the mobile device needs to perform recognition. These pre-processed models are downloaded to the mobile device in advance, enabling fast local inference without uploading entire images during the recognition moment.
Solution Approach 2:
The server extracts only the essential discriminative features from training images to create compact feature descriptors and classifiers. Instead of transmitting or processing complete images, only the extracted feature representations are downloaded to the mobile device, significantly reducing data transmission requirements while maintaining recognition accuracy.
2Measurement precision
If the mobile device uploads images to a server for location recognition, then the recognition accuracy is improved, but the bandwidth consumption increases
Solution Approach 1:
The server extracts only the essential discriminative features from training images to create compact feature descriptors and classifiers. Instead of transmitting or processing complete images, only the extracted feature representations are downloaded to the mobile device, significantly reducing data transmission requirements while maintaining recognition accuracy.
Solution Approach 2:
The server creates compact copies of the recognition models (classifiers and feature descriptors) that can be downloaded once and reused for multiple recognition tasks. These compact model copies replace the need to transmit or process full-resolution images during inference, dramatically reducing bandwidth consumption.
3Measurement precision
If the server stores and processes complete images for matching, then the recognition accuracy is improved, but the computational resources and storage requirements increase
Solution Approach 1:
The server extracts only the essential discriminative features from training images to create compact feature descriptors and classifiers. Instead of transmitting or processing complete images, only the extracted feature representations are downloaded to the mobile device, significantly reducing data transmission requirements while maintaining recognition accuracy.
Solution Approach 2:
The patent transforms images from their original high-dimensional pixel space into compact feature descriptor space through dimensionality reduction and feature extraction. This parameter transformation maintains the essential discriminative information while drastically reducing the computational and storage requirements for both server and mobile device.
Data Source
AI summary
A mobile device having the capability of performing real-time location recognition with assistance from a server is provided. The approximate geophysical location of the mobile device is uploaded to the server. Based on the mobile device's approximate geophysical location, the server responds by sending the mobile device a message comprising a classifier and a set of feature descriptors. This can occur before an image is captured for visual querying. The classifier and feature descriptors are computed during an offline training stage using techniques to minimize computation at query time. The classifier and feature descriptors are used to perform visual recognition in real-time by performing the classification on the mobile device itself.